{"id":3280022,"url":"https://alion.io/job/symphoni-hr-data-analytics-and-ai-engineer","title":"Data Analytics and AI Engineer","company":{"id":3800108,"name":"Symphoni HR","domain":"symphonihr.com","url":"https://alion.io/company/symphoni-hr","size_band":"201-500","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Schema","truth_index":{"grade":"A","score":93,"open_postings":40,"ghost_share":0,"stale_share":0,"repost_share":0.5,"time_to_fill_p50_days":45,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Ahmedabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":16500,"max_usd":34000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":41},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Hadoop","optional":false},{"name":"IAM","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"Looker","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Tokenization","optional":false}],"status":"live","first_seen_at":"2026-10-26T16:03:24Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-29T07:29:45Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"This role bridges a mature big data environment and a future AI-ready cloud ecosystem. You will maintain and optimize existing data pipelines while re-architecting workflows onto AWS and Databricks. You will also contribute to building intelligent AI agents and LLM-powered applications that deliver immediate business value.\nOur client is a leading global software company in the computer software industry, serving enterprises worldwide with data, analytics and technology solutions. They operate at significant scale with a strong focus on innovation and modern cloud platforms. The culture emphasizes continuous learning, engineering excellence and delivering measurable business value.\nResponsibilities\nRe-architect legacy Apache Spark and Hadoop MapReduce workflows into optimized, production-ready pipelines on AWS and Databricks.\nDesign, build and deploy AI agents and workflow automation tools leveraging Large Language Models such as Anthropic Claude.\nBuild and optimize pipelines for AI use cases, including unstructured data ingestion, feature tokenization and metadata tagging for vector databases.\nMonitor, maintain and troubleshoot big data workloads on Hadoop clusters, resolving bottlenecks and out-of-memory errors during migration.\nServe as engineering liaison for BI stakeholders, performing enhancements, bug fixes and data extract optimizations to reduce dashboard latency.\nUse Databricks and Delta Lake features such as ACID transactions, Z-Ordering and caching to improve pipeline performance, reliability and cost efficiency.\nLeverage Databricks Vector Search, Mosaic AI and Lakeflow to orchestrate and serve production-grade Generative AI and RAG applications.\nRequirements\n5 to 8 years of professional software or data engineering experience in production environments.\nHands-on experience building AI agents, integrating LLM APIs (notably Anthropic Claude) and using orchestration frameworks such as LangChain or Databricks Mosaic AI.\nSolid hands-on experience with Apache Spark, including understanding of partitioning, shuffling, caching and broadcast joins.\nStrong proficiency in Python (PySpark) or Scala and intermediate-to-advanced SQL (window functions, query tuning, complex joins).\nDirect experience or deep knowledge of the AWS ecosystem (S3, IAM) and Databricks environments.\nPractical experience with any BI tool such as Tableau, Looker or Power BI, including debugging calculated fields and troubleshooting slow reports.\nBachelor's degree in Computer Science, Data Engineering, Information Systems or a related quantitative field, or equivalent practical experience.\nSkills\nApache Spark, Hadoop (HDFS, MapReduce, Hive), Python (PySpark) or Scala, SQL, AWS (S3, IAM), Databricks (Delta Lake), LLM APIs (Anthropic Claude), LangChain / AI agent orchestration frameworks, Tableau or other BI tools, AWS Glue, AWS EMR, Airflow, Control-M, Unity Catalog, Databricks Vector Search, Databricks Mosaic AI, Lakeflow, Copilot, RAG applications, Looker, Power BI","description_format":"text","description_chars":2991,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-26T16:03:24Z"}],"visa":[],"liveness":{"score":54,"band":"ok","label":"Likely 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